Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images
This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with t...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2014-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/101/2014/isprsarchives-XL-3-101-2014.pdf |
| Summary: | This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised
classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions.
Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB
color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for
supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using
the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is
collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard
deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological
operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of
high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the
proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate
the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected
from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and
853 % overall pixel-based and object-based precision performances, respectively. |
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| ISSN: | 1682-1750 2194-9034 |